One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

This chapter addresses the role that data‐driven approaches, especially machine learning
methods, are expected to play in materials research in the immediate future. Machine …

In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

With unprecedented amounts of materials data generated from experiments as well as high-
throughput density functional theory calculations, machine learning techniques has the …

Machine learning (ML) and artificial intelligence (AI) are quickly becoming commonplace in
materials research. In addition to the standard workflow of fitting a model to a large set of …

We are excited to present this Special Topic collection on Machine Learning for Materials
Design and Discovery in the Journal of Applied Physics. With a wide range of exciting and …